Unverified50% confidenceFactExact time
Pistis-Agentic builds on Thinking by introducing agent trajectory data, supporting long-horizon planning, iterative reasoning, and tool invocation.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/25/2026
First Seen
Valid until: 12/24/2026
Sources
Related Entities
Related Claims
UnverifiedPistis-Thinking focuses on strengthening deep multimodal reasoning capabilities.74% similarUnverifiedIn each iteration of the loop, an agent needs to repackage the system prompt, conversation history, tool descriptions, and all outputs from previous steps before feeding them into the model, causing token consumption to grow linearly or even super-linearly with the number of steps.69% similarUnverifiedThe core idea of inference-time compute is to have the model generate longer chains of thought at inference time, improving accuracy on complex tasks through self-verification and multi-path search, which has a methodological connection to the success of Monte Carlo Tree Search in game-playing AI—AlphaGo surpassed human players through large-scale tree search at inference time.68% similarUnverifiedPaths for improving model intelligence include increasing training and inference computation, optimizing reasoning architectures, and strengthening chain-of-thought training.68% similarUnverifiedThe task planning process is typically realized through chain-of-thought reasoning and task decomposition, while more advanced implementations adopt a tree-of-thought framework to simultaneously explore multiple planning paths.67% similar
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